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This exploratory approach improves the survey as it helps to focus the questions on topics that are relevant or important to participants.Įxplanatory sequential design uses the findings of quantitative data to plan qualitative data collection. A good example would be using interview or focus group results to design survey questions. In exploratory sequential design, we can collect and analyze qualitative data and use the findings to inform upcoming quantitative data collection. In this article, we discuss how qualitative and quantitative data can be integrated at the study design level, methods, or analysis level.Īt the design level, data can be collected concurrently, or one approach can be used to inform the other. They are both great but very different beverages. To give you a simple analogy, “mixed methods” is like mixing coffee and milk together (e.g., latte), while “multiple methods” is having coffee and milk separately. “Mixed methods” is intentionally using one data source with another, with the purpose of triangulating the results, whereas “multiple methods” is simply using different data collection strategies in the same program, but with no intention to "mix" or integrate them. Depending on the scope of the evaluation, we often collect large amounts and different types of data, and we must triangulate them to get to the main evaluation findings. The ability to synthesize large amounts of data to identify important information is an essential skill for evaluators. It can also help to discover contradictions and inconsistencies that otherwise might not have been revealed between different sources and can clarify the results of an evaluation. Data integration can enhance reliability in evaluation findings (e.g., by increasing the ability of findings to be replicated). Data integration is a way of merging these data from different sources through mixed methods. In evaluation, we use multiple types and sources of data, diverse methods of collection, or multiple evaluators to answer evaluation questions.